In recent decades, technological change and the global integration it enables have been rapidly reshaping the U.S. economy. These forces have improved the potential of some individuals to thrive, but diminished prospects for others striving to reach or maintain their place in the American middle class. Amid these changes, how and where will individuals find durable sources of good jobs?

Certainly, education is an important part of the picture, particularly for enabling upward mobility among young people. But tens of millions of adults who are already a critical part of the American workforce also deserve a chance to obtain better jobs, with higher pay and benefits.

This report shows that the industrial structure and growth of metropolitan economies—in particular, whether they provide sufficient numbers of jobs in opportunity industries—matters greatly for workers’ ability to get ahead economically. It examines the presence of occupations and industries in the nation’s 100 largest metropolitan areas that either currently or over time provide workers access to stable middle-class wages and benefits, particularly for the 38 million prime-age workers without a bachelor’s degree.

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By Chad Shearer, Isha Shah
In recent decades, technological change and the global integration it enables have been rapidly reshaping the U.S. economy. These forces have improved the potential of some individuals to thrive, but diminished prospects for others striving to reach or maintain their place in the American middle class. Amid these changes, how and where will individuals find durable sources of good jobs?
Certainly, education is an important part of the picture, particularly for enabling upward mobility among young people. But tens of millions of adults who are already a critical part of the American workforce also deserve a chance to obtain better jobs, with higher pay and benefits.
This report shows that the industrial structure and growth of metropolitan economies—in particular, whether they provide sufficient numbers of jobs in opportunity industries—matters greatly for workers’ ability to get ahead economically. It examines the presence of occupations and industries in the nation’s 100 largest metropolitan areas that either currently or over time provide workers access to stable middle-class wages and benefits, particularly for the 38 million prime-age workers without a bachelor’s degree.
Click here to download detailed data for metro areas »
Interactive by Alec FriedhoffBy Chad Shearer, Isha Shah
In recent decades, technological change and the global integration it enables have been rapidly reshaping the U.S. economy. These forces have improved the potential of some individuals to thrive, but diminished prospects ... https://www.brookings.edu/blog/usc-brookings-schaeffer-on-health-policy/2018/12/07/the-essential-scan-top-findings-in-health-policy-research-52/The essential scan: Top findings in health policy researchhttp://webfeeds.brookings.edu/~/584221736/0/brookingsrss/topics/healthit~The-essential-scan-Top-findings-in-health-policy-research/
Fri, 07 Dec 2018 14:27:11 +0000https://www.brookings.edu/?p=551813

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By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg

What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the latest research and what it means for policymakers. If you’d like to receive the biweekly Essential Scan by email, you can sign up here.

Previous reports and anecdotal evidence have suggested that the administrators of long-term care hospitals (LTCHs) face financial incentives to keep patients hospitalized for longer than is medically necessary in order to collect a larger payment from the Medicare Prospective Payment System (PPS). A new study which uses Medicare claims data from 2004-2013 confirms this speculation, finding that LTCHs discharge 25.7 percent of patients in the three days immediately after the LTCH has secured a larger Medicare reimbursement (a threshold called the magic day by industry participants) while only 6.8 percent of patients are discharged in the three days before the larger reimbursement is secured. The authors of the study are able to show that this significant spike in discharges is a direct result of strategic decisions made by LTCHs by showing that their findings are consistent across Diagnostic Related Groups and by showing that this behavior did not occur before the current payment rules were implemented. Finally, the authors develop a dynamic structural model of LTCHs discharge decisions to evaluate a 2014 recommendation by MedPAC to address this behavior and find that it would save about $500 million per year for the nine biggest DRGs alone and would lead LTCHs to release patients a week earlier on average. Full study here.

Discharge Patterns for DRG 207 by LTCH Profit Type, FY 2004-2013

The ACA was enacted with the goal of expanding access to affordable health insurance for millions of Americans. However, many feared that increasing the number of people eligible for public health insurance might reduce the number of hours workers chose to work. New research from the Urban Institute seeks to determine whether coverage gains from 2010 to 2016 were associated with changes in labor market outcomes across occupations. The researchers estimate that about 10.6 million workers were added to the workforce between 2010 and 2016, and nearly all occupations gained workers during this time. They find that occupations that experienced greater coverage gains under the ACA were not more likely to experience adverse labor market consequences. The study also found that larger coverage gains under the ACA were not associated with lower real earnings or hours worked—across all occupations. Despite many predictions that employment and work hours would be reduced, the ACA seems to not have negatively affected either. Full study here.

Telemedicine-enabled virtual visits have been promoted as a tool to increase access to specialty care and lower costs. New research looks at data from over 35,000 patients in a Massachusetts-based ACO, comparing patients who registered with the virtual visit program and used it to those who registered but did not use the program. The researchers found that virtual visits reduced in-person visits by 33 percent, but increased total visits (virtual plus in-person) by 80 percent over one and a half years. They estimate that at the population level for every 3.5 virtual visits, one in-person visit was averted. However, after one year the rate of in-person care among users of the virtual visit program returned to baseline levels and the use of virtual visits declined. If virtual visit programs are intended to be a tool in accessing specialty care, this study suggests they will need to find ways to extend their benefits past the one-year mark. Full study here.

A number of recently introduced programs and reforms have aimed to create cost control incentives through disrupting the traditional fee-for-service paradigm. In Health Maintenance Organizations (HMOs), the HMO is paid a fixed amount per enrollee, regardless of care used. HMOs have been found to have lower spending levels compared to other plans, but it is unclear whether the high price sensitivity seen in HMOs is the result of provider price sensitivity or price sensitive patients selecting into these plans. A new study aims to look at patient and provider price sensitivity in HMOs through analyzing demand for statins. According to the study, spending on statins was 19 percent lower (a difference of about $95) in HMOs compared to other insurance plans. The researcher finds evidence that HMO patients are nearly twice as sensitive to their drug’s copay as non-HMO patients. In addition, HMO physicians were found to be highly sensitive to drug placement on the formulary and drug procurement costs, which together accounted for 20 to 55 percent of the spending difference compared to other insurance plans. Because there are many generic and branded options for reducing cholesterol, the generalizability of these findings may be limited, but better understanding the mechanisms through which HMOs achieve cost savings continues to be important as alternative payment models are considered. Full study here.

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https://www.brookings.edu/wp-content/uploads/2017/06/es_20170621_hospitalhall.jpg?w=270By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg
What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the latest research and what it means for policymakers. If you’d like to receive the biweekly Essential Scan by email, you can sign up here.
Medicare Prospective Payment System Distorts Patient Care Decisions at Long-Term Care Hospitals
Study by: Paul J. Eliason, Paul L. E. Grieco, Ryan C. McDevitt, and James W. Roberts
Previous reports and anecdotal evidence have suggested that the administrators of long-term care hospitals (LTCHs) face financial incentives to keep patients hospitalized for longer than is medically necessary in order to collect a larger payment from the Medicare Prospective Payment System (PPS). A new study which uses Medicare claims data from 2004-2013 confirms this speculation, finding that LTCHs discharge 25.7 percent of patients in the three days immediately after the LTCH has secured a larger Medicare reimbursement (a threshold called the magic day by industry participants) while only 6.8 percent of patients are discharged in the three days before the larger reimbursement is secured. The authors of the study are able to show that this significant spike in discharges is a direct result of strategic decisions made by LTCHs by showing that their findings are consistent across Diagnostic Related Groups and by showing that this behavior did not occur before the current payment rules were implemented. Finally, the authors develop a dynamic structural model of LTCHs discharge decisions to evaluate a 2014 recommendation by MedPAC to address this behavior and find that it would save about $500 million per year for the nine biggest DRGs alone and would lead LTCHs to release patients a week earlier on average. Full study here.
Discharge Patterns for DRG 207 by LTCH Profit Type, FY 2004-2013
(Source: AER)
________________________________________________________
No Evidence that ACA Decreased Labor Supply
Study by: Anuj Gangopadhyaya, Bowen Garrett, and Stan Dorn
The ACA was enacted with the goal of expanding access to affordable health insurance for millions of Americans. However, many feared that increasing the number of people eligible for public health insurance might reduce the number of hours workers chose to work. New research from the Urban Institute seeks to determine whether coverage gains from 2010 to 2016 were associated with changes in labor market outcomes across occupations. The researchers estimate that about 10.6 million workers were added to the workforce between 2010 and 2016, and nearly all occupations gained workers during this time. They find that occupations that experienced greater coverage gains under the ACA were not more likely to experience adverse labor market consequences. The study also found that larger coverage gains under the ACA were not associated with lower real earnings or hours worked—across all occupations. Despite many predictions that employment and work hours would be reduced, the ACA seems to not have negatively affected either. Full study here. ________________________________________________________
Benefits of Telemedicine Quickly Fade for Massachusetts ACO
By: Sachin J. Shah, Lee. H. Schwamm, Adam B. Cohen, Marcy R. Simoni, Juan Estrada, Marcelo Matiello, Atheendar Venkataramani, and Sandhya K. Rao
Telemedicine-enabled virtual visits have been promoted as a tool to increase access to specialty care and lower costs. New research looks at data from over 35,000 patients in a Massachusetts-based ACO, comparing patients who registered with the virtual visit program and used it to those who registered but did not use the program. The researchers found that virtual visits reduced in-person visits by 33 percent, but increased total visits (virtual plus in-person) by 80 percent over one and ... By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg
What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the ... https://www.brookings.edu/research/medicare-graduate-medical-education-funding-is-not-addressing-the-primary-care-shortage-we-need-a-radically-different-approach/Medicare graduate medical education funding is not addressing the primary care shortage: We need a radically different approachhttp://webfeeds.brookings.edu/~/583491950/0/brookingsrss/topics/healthit~Medicare-graduate-medical-education-funding-is-not-addressing-the-primary-care-shortage-We-need-a-radically-different-approach/
Mon, 03 Dec 2018 14:34:52 +0000https://www.brookings.edu/?post_type=research&p=550782

A growing body of evidence shows that areas in the United States with robust primary care systems tend to have better outcomes and lower per capita costs than areas that rely more on specialists.1 Over the past several decades, the US medical education system has produced an increasingly specialized physician workforce without any strategic direction toward achieving a socially desirable mix of primary care physicians (PCPs) and specialists.2 At the same time, health care reforms, such as patient-centered medical homes and Accountable Care Organizations (ACOs), rely more on PCPs and other providers who are equipped to coordinate their own care with the care of specialists. Demographic trends signal a growing need for such coordination as the population ages and patients with multiple chronic conditions become more prevalent.3 Despite these trends, physicians in training tend not to select primary care or related specialties.

However, Medicare’s payment system may tend to skew the choices made by doctors when selecting residency programs and entering into medical practice. In 1992, Congress changed the method of reimbursing physicians from a system based on individual physicians’ historical charges to one based on the “relative values” of the thousands of services physicians charge Medicare for. This meant that while fees for existing services have increased very little over the past several years, high payments for new services coupled with substantial increases in the volume of expensive diagnostic and other procedures, have widened the income gap between physician specialties.

We conclude that the mix of physicians in the US has too few PCPs and too many specialists, the income gap between the two is a major determinant of the PCP/specialty mix, and Medicare physician payment policy is a major contributor to the income gap. Changes in GME payments to hospitals to favor training of PCPs have little potential to make a meaningful difference because other incentives affecting physicians in training and teaching hospitals are too powerful. We find that loan forgiveness policies for medical students who pursue careers in primary care appear to be a promising path towards closing the gap and incentivizing more medical students to pursue careers as PCPs. However, no approaches focused on medical education are likely to be successful without revamping the Medicare relative value scale so that it contributes less to the physician income gap.

The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. They are currently not an officer, director, or board member of any organization with an interest in this article.

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By Bruce Steinwald, Paul Ginsburg, Caitlin Brandt, Sobin Lee, Kavita Patel
A growing body of evidence shows that areas in the United States with robust primary care systems tend to have better outcomes and lower per capita costs than areas that rely more on specialists.1 Over the past several decades, the US medical education system has produced an increasingly specialized physician workforce without any strategic direction toward achieving a socially desirable mix of primary care physicians (PCPs) and specialists.2 At the same time, health care reforms, such as patient-centered medical homes and Accountable Care Organizations (ACOs), rely more on PCPs and other providers who are equipped to coordinate their own care with the care of specialists. Demographic trends signal a growing need for such coordination as the population ages and patients with multiple chronic conditions become more prevalent.3 Despite these trends, physicians in training tend not to select primary care or related specialties.
In Medicare Graduate Medical Education Funding is Not Addressing the Primary Care Shortage: We Need a Radically Different Approach we investigate whether and to what extent the ways Medicare pays for physician and hospital services and subsidizes graduate medical education (GME) in teaching hospitals either fosters or acts as an impediment to achieving a mix of specialists and PCPs consistent with more effective and efficient delivery of health care.4 Looking at this issue through the lens of the medical education marketplace, we conclude that Medicare’s GME subsidies have relatively little effect on specialty mix.
However, Medicare’s payment system may tend to skew the choices made by doctors when selecting residency programs and entering into medical practice. In 1992, Congress changed the method of reimbursing physicians from a system based on individual physicians’ historical charges to one based on the “relative values” of the thousands of services physicians charge Medicare for. This meant that while fees for existing services have increased very little over the past several years, high payments for new services coupled with substantial increases in the volume of expensive diagnostic and other procedures, have widened the income gap between physician specialties.
We conclude that the mix of physicians in the US has too few PCPs and too many specialists, the income gap between the two is a major determinant of the PCP/specialty mix, and Medicare physician payment policy is a major contributor to the income gap. Changes in GME payments to hospitals to favor training of PCPs have little potential to make a meaningful difference because other incentives affecting physicians in training and teaching hospitals are too powerful. We find that loan forgiveness policies for medical students who pursue careers in primary care appear to be a promising path towards closing the gap and incentivizing more medical students to pursue careers as PCPs. However, no approaches focused on medical education are likely to be successful without revamping the Medicare relative value scale so that it contributes less to the physician income gap.
Read the full paper here. ________________________________________________________
The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. They are currently not an officer, director, or board member of any organization with an interest in this article. By Bruce Steinwald, Paul Ginsburg, Caitlin Brandt, Sobin Lee, Kavita Patel
A growing body of evidence shows that areas in the United States with robust primary care systems tend to have better outcomes and lower per capita costs than areas that ... https://www.brookings.edu/blog/usc-brookings-schaeffer-on-health-policy/2018/11/28/the-trump-administration-side-stepped-rulemaking-processes-on-the-acas-state-innovation-waivers-and-it-could-make-their-new-section-1332-guidance-invalid/The Trump administration side-stepped rulemaking processes on the ACA’s State Innovation Waivers—and it could make their new section 1332 guidance invalidhttp://webfeeds.brookings.edu/~/582722020/0/brookingsrss/topics/healthit~The-Trump-administration-sidestepped-rulemaking-processes-on-the-ACA%e2%80%99s-State-Innovation-Waivers%e2%80%94and-it-could-make-their-new-section-guidance/
Wed, 28 Nov 2018 19:14:31 +0000https://www.brookings.edu/?p=550202

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By Christen Linke Young

Section 1332 of the Affordable Care Act allows states to seek a waiver of various ACA requirements if the state can demonstrate that its proposal would not reduce the number of people covered or the affordability or comprehensiveness of that coverage, or increase federal deficits. For the last several years, the Department of Health and Human Services and the Department of the Treasury have worked to give meaning to this statutory instruction.

A recent guidance document issued in late October by the two agencies represents the latest foray. It claims to make significant new policy, primarily by loosening the standards for comprehensive, affordable coverage. But importantly, the agencies chose to release it as guidance, rather than as a rule subject to the typical notice and comment rulemaking process. There are serious questions about whether the policy articulated in the guidance is a permissible interpretation of the underlying statute, but, at the very least, it is likely invalid for the agency to attempt to make this policy without a full rulemaking process.

Specifically, by releasing the document as guidance, the agencies are implicitly taking the position that it is an “interpretative rule” exempt from the standard rulemaking process under the Administrative Procedure Act (APA). However, the new guidance contains policy that would likely be classified under the APA as a “legislative rule.” As a result, the agencies likely cannot adopt these changes without notice and comment rulemaking, and the guidance may be invalid. Claims that the guidance is procedurally invalid open up potential new avenues for litigation surrounding section 1332 in general and could also become relevant to challenges regarding an approved waiver.

Since 1946, the Administrative Procedure Act has outlined the process agencies must follow when promulgating rules. The statute requires a standard notice and comment process for all policymaking, but exempts “interpretative” rules from these procedures.[1] Thus, courts have come to distinguish between “legislative” or “substantive” rules that do require notice and comment under the APA, and interpretative rules that do not.

It is important to emphasize that an agency action described as interpreting language that appears elsewhere is not necessarily an “interpretative rule.” Indeed, much of what agencies do in their traditional notice and comment rulemaking is focused on interpreting the statutes under their jurisdiction. As just one example, in a sweeping rulemaking process that began in 2009, the EPA concluded it had authority to and would begin regulating greenhouse gases. To accomplish that major policy change, the EPA was, formally, interpreting the term “air pollutant” in the Clean Air Act to include carbon dioxide. But a court would be quite unlikely to conclude that an agency could begin to regulate a major segment of the U.S. economy (and implement the result of a years-long legal battle) with a simple guidance document. Significant actions like this will require notice and comment under the APA.

Instead, courts have construed the term “interpretative rule” to mean something narrower.

In determining whether a rule is interpretative, courts generally start with the formulation that legislative rules have the “force and effect of law” while interpretative rules “advise the public of the agency’s construction of the statutes and rules which it administers.”[2] To give meaning to this distinction, courts have considered a number of factors:

Substantive change: A common thread in many cases is an attempt to understand whether the guidance is a meaningful change from the statute or existing legislative rule. Courts have looked to whether the guidance “effects a substantive regulatory change to the statutory or regulatory regime,”[3] or whether it “expand[s] the footprint of a regulation.”[4]

Nature of the interpretative leap: How significant is the agency’s interpretative leap? As one court explained, “the distinction between an interpretative rule and substantive rule … likely turns on how tightly the agency’s interpretation is drawn linguistically from the actual language of the statute.”[5] The more ambiguous the term the agency purports to interpret, and the more convoluted the agency’s interpretation is, the less likely the guidance is to be considered interpretative.

Legislative basis: Courts have looked to whether, in the absence of the supposedly interpretative rule, there would be an “adequate legislative basis”[6] for the agency’s future action. That is, if the agency had never issued their guidance, would they still have grounding in the statute or in a legislative rule for doing whatever it is the guidance promises they will do? If not, then the guidance will not be considered interpretative.

Rights and duties: Another formulation that courts have used is whether the guidance “imposes new rights or duties”[7] that would not otherwise exist.

Recent guidance on the ACA’s Section 1332 State Innovation Waivers is only valid if the waiver is an interpretative rule

Section 1332 of the ACA allows the federal government to grant states a waiver of various ACA requirements if the state can show they have an alternative plan that “will provide coverage that is at least as comprehensive…, will provide coverage and cost-sharing protections against excessive out-of-pocket spending that are at least as affordable…, will provide coverage to at least a comparable number of its residents…, [and] will not increase the federal deficit.”[8]

Comprehensiveness, affordability, coverage, and deficit neutrality are broad concepts. Since the passage of the ACA, stakeholders have wondered how to determine if a state plan satisfies these guardrails. In 2015, the federal government released a guidance document that explained the approach it would take in evaluating a state’s proposal.[9]

In October 2018, the government released new guidance that makes significant policy changes and expressly “supersedes” the 2015 guidance.[10] The primary thrust of the 2018 guidance is to weaken the comprehensiveness, affordability, and coverage guardrails, i.e., it states that the federal government would now deem a state plan to satisfy the comprehensiveness, affordability, and coverage tests that would have failed under the 2015 approach. But because neither guidance document was adopted using the notice and comment procedures in the APA, the guidance is only valid if it can be considered an interpretative rule under the APA.[11]

The 2018 guidance does not appear to be an interpretative rule

Much of the 2018 guidance does not seem consistent with the standards courts have articulated for an interpretative rule. The guidance makes substantial new policy that is a departure from the underlying statute, and thus should not be considered a valid interpretative rule.

Much of the 2018 guidance does not seem consistent with the standards courts have articulated for an interpretative rule.

Start by considering the agencies’ own description of the 2018 guidance document. “This guidance,” the agencies write, “intends to expand state flexibility.”[12] Elsewhere, the document explains that the guidance “aims to lower barriers” and that the agencies “are seeking to reduce burdens.” (Contrast that with parallel language in the 2015 guidance, which explains that the document “provides additional information about” the 1332 guardrails.) While a court will look deeper than this sort of precatory text, it is illuminating that the agencies say they expressly intended to expand the scope of 1332 waivers—and difficult to argue that they could satisfy that objective without affecting the rights and duties of third parties or “expanding the footprint” of the regulatory scheme.

The “Made Available” test

The most impactful policy in the new guidance affects the affordability and comprehensiveness guardrails. The 2015 guidance had explained that affordability and comprehensiveness would be measured by looking at what sort of health insurance coverage individuals would actually become enrolled in under the state’s proposed plan. The agencies articulated that this was a straightforward interpretation of the language in the statute, which directs the agencies to consider whether the state plan “will provide coverage” that is comprehensive and affordable. The 2018 guidance constructs a novel analysis—asking not what sort of coverage people will actually have, but rather what coverage is made “available” to people. Under the 2018 guidance, a state’s plan will be considered to satisfy the comprehensiveness and affordability guardrails even if residents will have less comprehensive or less affordable coverage compared to the status quo, as long as more comprehensive and more affordable coverage exist for individuals to hypothetically become enrolled in.

This new test is a significant departure from the statutory text. The agencies are reading the phrase “will provide coverage” to mean “will make available coverage.” If a court is asked to consider whether this is a valid interpretative rule, it will, as noted above, look to “how tightly the agency’s interpretation is drawn linguistically from the actual language of the statute.” It’s difficult to believe that the invention of the “made available” concept could survive this analysis. Put another way, one who is in fact provided comprehensive coverage is in a very different position from one who merely has that coverage made available to them—and so the guidance represents a major change from what the plain text of the statute more clearly envisions.

Defining coverage

The 2018 guidance also has a significant impact on a third 1332 guardrail: coverage. To analyze whether their plan meets this guardrail, states must quantify the number of people projected to have health coverage under the waiver. But to do so, states need to know what sort of health care arrangements “count” as coverage within the meaning of 1332. In the new guidance, the agencies purport to adopt a definition that classifies many kinds of health benefits as coverage—even if the benefit is limited and does not provide meaningful financial protection from medical costs. Specifically, the agencies explain that “in line with the Administration’s priority favoring private coverage, including [Association Health Plans] and [Short-Term Limited-Duration Insurance] plans,” they are broadening the definition of coverage to include those benefits. They accomplish this by defining coverage under 1332 as anything that would count as “health insurance coverage” under a 1997 regulation associated with implementation of HIPAA, which was intended to cast a wide net for regulation by federal and state law.[13]

As above, this change to the coverage guardrail is a significant interpretative leave from the plain text of the statute, and it inflicts a major substantive change to the program laid out in law. The statutory guardrails are structured to ensure that a state’s proposed waiver plan will result in outcomes that are broadly similar to the outcomes under the ACA. But allowing these sorts of plans—especially short-term limited-duration benefits—to count as “coverage” erodes that vision. The agencies are reaching for an unrelated cross reference to implement a meaningful new policy. Further, the fact that it is definitional does not render it an interpretative rule—the agencies explain that the change is intended to make short-term and association-based plans “count” under 1332, and that sort of significant new policy is exactly the type of change that makes a rule legislative rather than interpretative.

Additional changes that disqualify the guidance from being an interpretive rule

The guidance makes additional changes that may have less sweeping policy impacts but raise the same sorts of questions:

The guidance concludes that for purposes of meeting the coverage guardrail, it is permissible for a state plan to cause a temporary reduction in the number of people covered, if that is made up over time. There is no statutory basis for this sort of exception, and it implicates the same kinds of concerns about the nature of the agencies’ interpretative leap.

The agencies introduce the concept of the “magnitude” of changes in affordability, explaining that a waiver that reduces overall affordability could still be approved if the changes do not make people “substantially worse off.” In addition to being extremely vague, this new policy attempts to expand the agencies’ authority beyond what is conferred to them in statute, which cannot be permitted in an interpretative rule.

The guidance also addresses the statutory requirement that a state applying for a waiver have enacted a “State law that provides for State actions under a waiver under this section.” The guidance explains that the agencies may consider a state to have met this requirement in situations where the state has enacted only a general law related to ACA implementation, not anything specific to the proposed waiver. As above, the agencies are purporting to give themselves new authority to approve waivers, beyond what is available in statute, and an interpretative rule cannot accomplish that result.

Differences between the 2015 guidance and the 2018 guidance

Whether or not the 2018 guidance is a valid interpretative rule does not turn on the validity of the prior guidance, issued in 2015. Nonetheless, it is instructive to contrast the two documents. The 2015 guidance did introduce some vocabulary that does not expressly appear in the text of section 1332. For example, it explained that the agencies would consider the impact of the waiver on specific vulnerable groups, and that the analysis of comprehensiveness would be rooted in the ten categories of essential health benefits defined elsewhere in the ACA. But these sorts of explanations are much more clearly attempts to “advise the public of the agency’s construction” of section 1332. Critically, section 1332 provides that the Secretaries of Health and Human Services and the Treasury “may” approve waivers that satisfy the guardrails, but does not obligate them to do so. Therefore, to the extent the 2015 guidance explained how the agencies would use the discretion that is clearly granted to them in statute, they were simply offering the public information. Put another way, there is a straightforward “legislative basis” for the agencies to consider these factors, because the statute clearly provides the agencies with the discretion to deny waivers. The 2018 guidance, on the other hand, purports to allow the agencies to approve waivers that are not contemplated by the statute. That kind of action implicates the factors courts looks to in defining a legislative rule: it does not have an existing legislative basis and it “expands the footprint” of the statutory scheme.

Implications of an invalid interpretative rule

If the 2018 guidance is in fact a legislative, rather than interpretative, rule, then the agencies’ attempt to adopt it without notice and comment is impermissible, and that may have significant implications for future litigation.

First, it is important to acknowledge that the analysis of whether a given policy is a valid interpretative rule can quickly start to converge with the analysis of whether that policy is permissible under the statute at all. As noted above, the bigger the interpretative leap being taken by the agency, the more likely a rule is to be legislative.

It’s also very possible, however, that the leap may be so large it cannot be sustained under the statute at all. Similarly, the conclusion that a policy “expands the footprint” of the statutory scheme, imposes new “rights and duties,” or requires a “legislative basis” raises questions about whether that action is permissible under the statute. Thus, there is not a sharp line between procedural objections to the guidance, on the grounds that is should have been adopted using notice and comment, and substantive objections that it should not have been adopted at all. And, indeed, many of the factors suggesting the 132 guidance is not an interpretative rule also suggest that it is simply not a permissible interpretation of the Affordable Care Act.

If the 2018 guidance is in fact a legislative, rather than interpretative, rule, then the agencies’ attempt to adopt it without notice and comment is impermissible, and that may have significant implications for future litigation.

However, the use of a potentially invalid interpretative rule may create distinct possibilities for challenging the guidance, separate from any challenge to the underlying content. Entities that can plausibly assert that they will bear costs now, prior to any waiver approval, because of the position taken in this guidance may seek to enjoin its application. For example, a city or county in a state considering a 1332 waiver may be able to make such an argument. Or, to the extent the guidance affects the way a state is required to submit materials to the agencies for a 1332 waiver proposal—even if the state’s proposal would not “invoke” the policy changes announced in the guidance—there may be circumstances where a state could make such an argument.

The existence of the 2018 guidance enhances the ability of affected parties to argue that the dispute is ripe for litigation, because the agencies have clearly articulated the action they intend to take in a way that a court can meaningfully adjudicate. The agencies’ recent announcement that they will release “templates” for states to use in submitting waivers[14] further supports this line of reasoning, as it shows the agencies continuing down a path and imposing burdens on entities involved in the 1332 process. Enjoining the guidance would not immediately affect whether a particular waiver could be approved, but it could limit the agencies’ ability to apply the novel criteria that appear in the guidance.

Additionally, potential procedural issues associated with this guidance could become relevant in litigation after a waiver was approved. As just one example, to the extent the agencies rely on policies articulated in the guidance, the court could declare the guidance invalid and remand a waiver to the agency for additional consideration. While the details of any such challenge will depend on the particular waiver approved and the nature of the agency action, procedural deficiencies with this guidance would strip the agency of any deference and make it more likely that objections to a waiver approval would succeed.

[1] 5 U.S.C. § 533 (b). Note that agencies often describe their actions as “guidance” or “subregulatory guidance.” Guidance is not a category recognized under the APA; the APA’s formal rulemaking processes apply to all agency policymaking activity unless a specific exception applies. Specific exceptions are available for military functions, agency management (personnel, property, etc), and “interpretative rules, general statements of policy, or rules of agency organization, procedure, or practice.” The exception for interpretative rules is the only plausible pathway for the kind of policy being made in the recent 1332 guidance. Thus, regardless of how the agencies label the document, they must have implicitly concluded it is an interpretative rule.

[11] From 1997 to 2015, a line of cases developed in the D.C. Circuit holding that once an agency had released an interpretative rule, future changes to that interpretation often require notice and comment. Thus, under that line of reasoning, the 2018 guidance would be impermissible without notice and comment simply because it was a major change. However, in March 2015 the Supreme Court unanimously struck down the D.C. Circuit’s approach and held that interpretative rules could be changed with other interpretative rules that did not require notice and comment. Perez v. Mortgage Bankers Ass’n, 575 U.S. ___ (2015). Therefore, the question is not whether the 2018 guidance is a change from the 2015 guidance, but rather whether the 2018 guidance is itself an interpretative rule.

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By Christen Linke Young
Section 1332 of the Affordable Care Act allows states to seek a waiver of various ACA requirements if the state can demonstrate that its proposal would not reduce the number of people covered or the affordability or comprehensiveness of that coverage, or increase federal deficits. For the last several years, the Department of Health and Human Services and the Department of the Treasury have worked to give meaning to this statutory instruction.
A recent guidance document issued in late October by the two agencies represents the latest foray. It claims to make significant new policy, primarily by loosening the standards for comprehensive, affordable coverage. But importantly, the agencies chose to release it as guidance, rather than as a rule subject to the typical notice and comment rulemaking process. There are serious questions about whether the policy articulated in the guidance is a permissible interpretation of the underlying statute, but, at the very least, it is likely invalid for the agency to attempt to make this policy without a full rulemaking process.
Specifically, by releasing the document as guidance, the agencies are implicitly taking the position that it is an “interpretative rule” exempt from the standard rulemaking process under the Administrative Procedure Act (APA). However, the new guidance contains policy that would likely be classified under the APA as a “legislative rule.” As a result, the agencies likely cannot adopt these changes without notice and comment rulemaking, and the guidance may be invalid. Claims that the guidance is procedurally invalid open up potential new avenues for litigation surrounding section 1332 in general and could also become relevant to challenges regarding an approved waiver.
Defining “interpretative rule” vs. “legislative rule” under the Administrative Procedure Act
Since 1946, the Administrative Procedure Act has outlined the process agencies must follow when promulgating rules. The statute requires a standard notice and comment process for all policymaking, but exempts “interpretative” rules from these procedures.[1] Thus, courts have come to distinguish between “legislative” or “substantive” rules that do require notice and comment under the APA, and interpretative rules that do not.
It is important to emphasize that an agency action described as interpreting language that appears elsewhere is not necessarily an “interpretative rule.” Indeed, much of what agencies do in their traditional notice and comment rulemaking is focused on interpreting the statutes under their jurisdiction. As just one example, in a sweeping rulemaking process that began in 2009, the EPA concluded it had authority to and would begin regulating greenhouse gases. To accomplish that major policy change, the EPA was, formally, interpreting the term “air pollutant” in the Clean Air Act to include carbon dioxide. But a court would be quite unlikely to conclude that an agency could begin to regulate a major segment of the U.S. economy (and implement the result of a years-long legal battle) with a simple guidance document. Significant actions like this will require notice and comment under the APA.
Instead, courts have construed the term “interpretative rule” to mean something narrower.
In determining whether a rule is interpretative, courts generally start with the formulation that legislative rules have the “force and effect of law” while interpretative rules “advise the public of the agency’s construction of the statutes and rules which it administers.”[2] To give meaning to this distinction, courts have considered a number of factors:
- Substantive change: A common thread in many cases is an attempt to understand whether the guidance is a meaningful change from the statute or ... By Christen Linke Young
Section 1332 of the Affordable Care Act allows states to seek a waiver of various ACA requirements if the state can demonstrate that its proposal would not reduce the number of people covered or the affordability or ... https://www.brookings.edu/blog/techtank/2018/11/19/who-should-profit-from-the-sale-of-patient-data/Who should profit from the sale of patient data?http://webfeeds.brookings.edu/~/581077940/0/brookingsrss/topics/healthit~Who-should-profit-from-the-sale-of-patient-data/
Mon, 19 Nov 2018 12:00:55 +0000https://www.brookings.edu/?p=548562

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By Niam Yaraghi

Patients’ medical data constitute a cornerstone of the big data economy. A multi-billion dollar industry operates by collecting, merging, analyzing and packaging patient data and selling it to the highest bidder. Data buyers range from health policy researchers to pharmaceutical companies and marketing corporations. While this industry has been quietly operating and accumulating profits for many years, patient advocacy groups have recently turned their attention to it. They argue that patients should own their medical records and therefore should be entitled to a fair share of the profits created through the sale of their data.

Data stewardship, not ownership

With one exception, every U.S. state either recognizes medical providers as the owners of medical data or do not have any laws to confer specific ownership or property right to medical records. Only New Hampshire explicitly grants ownership of data to patients. Regardless of state law, I believe that we must abandon the discussion of data ownership and instead focus on data stewardship. The ownership of data, whether granted to patients or to providers, will have dangerous unintended consequences.

Patients’ ownership of data implies that they would have a right to change their medical data as they wish. For example, an individual could edit the results of a blood test to show lower levels of cholesterol when applying for a life insurance policy, or refuse the Centers for Disease Control (CDC) access to medical records and undermine the agency’s efforts to predict and manage the outbreak of viral diseases. On the other hand, providers’ ownership of data implies that they can destroy data without notifying patients or refuse to share them with oversight agencies if a malpractice lawsuit is brought against them.

A model of data stewardship alleviates these concerns. Once a party takes the stewardship of data, they have to act according to a set of rules which guarantee the benefits of all other parties. Despite some notable exceptions such as the Apple Health Application in which patients will be in charge of storing and sharing of their medical records, medical providers are designated as de facto stewards of patients’ medical data in the U.S. healthcare system. This aligns with the long term vision of policymakers who authorized billions of dollars to incentives medical providers to adopt Electronic Health Records (EHR) systems so that they could collect, store, and share patients’ medical data in electronic format.

Data management is an expensive and risky business

The major challenge of the data stewardship model is the fair compensation of the steward. It is very expensive to store digital data: The costs of implementing an EHR system at a hospital network can exceed 1 billion dollars. Providers not only have to pay for upfront technology implementation, but also continuously invest in maintaining their systems and ensuring their security. Additionally, they bear the risks of potential privacy breaches which are extremely common in the healthcare sector and have significant financial and organizational consequences for health care providers.

Healthcare providers, or any other agency, will not invest in building and maintaining technology platforms for collecting and maintain medical data unless they have adequate economic incentives to do so. To recoup these costs, medical providers should either charge patients directly for their data management services, or be allowed to monetize such data. A system in which patients are neither willing to pay providers for keeping their data nor willing to allow the providers to monetize their data cannot succeed financially.

Sharing profits IMposes Extra Costs, privacy risk

Sharing of profits with patients has many challenges. First, although patient data constitute the raw material necessary for data mining, it has very limited value before processing. It is the aggregation, merging, and analyses of such data that creates value. For example, the hospitalization history of a patient on its own provides limited information. However, when such data are merged with the patient’s family history and compared to similar data of a large group of patients, one can infer the chance of re-admission for the patient, which will be of significant medical and financial value. In other words, the value of patient data emerges only after significant processing. This makes it very difficult to assess the fair value of any single patient’s data.

Second, even if one could successfully assess the fair value of patients’ data, distributing the fair share of profits to patients would require a sophisticated tracking and accounting system far more complicated than that of the Internal Revenue Service (IRS). The cost of implementing this system will significantly eat into profits and further reduce the amount that patients receive. More importantly, such a system would be a significant threat to patients’ privacy because it will require identification of patients in order to make financial transactions with them.

A NeW Model For DATA Sharing

An overwhelming majority of patients are willing to share their medical data with patients, doctors, researchers and even pharmaceutical companies. That is why in almost all of the health information exchange organizations in the U.S., most patients consent to sharing of their medical records. The indirect benefits that patients receive from sharing of their records, such as access to newly developed life-saving drugs or targeted marketing, could easily surpass the small financial benefits that they could receive from the sale of their data. The benefits of disclosing health information are not necessarily medical or economic: Once individuals are given the choice, pure altruistic motives will be strong enough for a majority of them to freely disclose their information.

There is potential for private businesses to build platforms that enhance the value of patient data and share the additional profits directly with patients. A good example is a platform for sharing patient data for research on Alzheimer’s disease. It is the nation’s most expensive disease, affecting over 5 million Americans each year, and yet still has no cure. Despite notable efforts, such as the Alzheimer’s Associations’ Trialmatch program, it is very difficult to find qualified patients to enroll in clinical trials.

A system in which interested individuals could share their detailed and identified medical records with Alzheimer’s researchers and pharmaceutical companies would both benefit society and save lives. The system could analyze data and alert patients if they qualify for a clinical trial, which would reduce the time and cost required to complete the trial. It could also diagnose the patient’s disease early or even find a treatment. Moreover, such system could easily financially reward patients for their data which are otherwise difficult to access. Policymakers should consider the long-term benefits of patients and refrain from imposing expensive regulations that could potentially slow down research and development activities in the healthcare sector.

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By Niam Yaraghi
Patients’ medical data constitute a cornerstone of the big data economy. A multi-billion dollar industry operates by collecting, merging, analyzing and packaging patient data and selling it to the highest bidder. Data buyers range from health policy researchers to pharmaceutical companies and marketing corporations. While this industry has been quietly operating and accumulating profits for many years, patient advocacy groups have recently turned their attention to it. They argue that patients should own their medical records and therefore should be entitled to a fair share of the profits created through the sale of their data.
Data stewardship, not ownership
With one exception, every U.S. state either recognizes medical providers as the owners of medical data or do not have any laws to confer specific ownership or property right to medical records. Only New Hampshire explicitly grants ownership of data to patients. Regardless of state law, I believe that we must abandon the discussion of data ownership and instead focus on data stewardship. The ownership of data, whether granted to patients or to providers, will have dangerous unintended consequences.
Patients’ ownership of data implies that they would have a right to change their medical data as they wish. For example, an individual could edit the results of a blood test to show lower levels of cholesterol when applying for a life insurance policy, or refuse the Centers for Disease Control (CDC) access to medical records and undermine the agency’s efforts to predict and manage the outbreak of viral diseases. On the other hand, providers’ ownership of data implies that they can destroy data without notifying patients or refuse to share them with oversight agencies if a malpractice lawsuit is brought against them.
A model of data stewardship alleviates these concerns. Once a party takes the stewardship of data, they have to act according to a set of rules which guarantee the benefits of all other parties. Despite some notable exceptions such as the Apple Health Application in which patients will be in charge of storing and sharing of their medical records, medical providers are designated as de facto stewards of patients’ medical data in the U.S. healthcare system. This aligns with the long term vision of policymakers who authorized billions of dollars to incentives medical providers to adopt Electronic Health Records (EHR) systems so that they could collect, store, and share patients’ medical data in electronic format.
Data management is an expensive and risky business
The major challenge of the data stewardship model is the fair compensation of the steward. It is very expensive to store digital data: The costs of implementing an EHR system at a hospital network can exceed 1 billion dollars. Providers not only have to pay for upfront technology implementation, but also continuously invest in maintaining their systems and ensuring their security. Additionally, they bear the risks of potential privacy breaches which are extremely common in the healthcare sector and have significant financial and organizational consequences for health care providers.
Healthcare providers, or any other agency, will not invest in building and maintaining technology platforms for collecting and maintain medical data unless they have adequate economic incentives to do so. To recoup these costs, medical providers should either charge patients directly for their data management services, or be allowed to monetize such data. A system in which patients are neither willing to pay providers for keeping their data nor willing to allow the providers to monetize their data cannot succeed financially.
Sharing profits IMposes Extra Costs, privacy risk
Sharing of profits with patients has many challenges. First, although patient data constitute the raw material necessary for data mining, it has very limited value before ... By Niam Yaraghi
Patients’ medical data constitute a cornerstone of the big data economy. A multi-billion dollar industry operates by collecting, merging, analyzing and packaging patient data and selling it to the highest bidder.https://www.brookings.edu/blog/usc-brookings-schaeffer-on-health-policy/2018/11/15/the-essential-scan-top-findings-in-health-policy-research-51/The essential scan: Top findings in health policy researchhttp://webfeeds.brookings.edu/~/580481478/0/brookingsrss/topics/healthit~The-essential-scan-Top-findings-in-health-policy-research/
Thu, 15 Nov 2018 21:06:27 +0000https://www.brookings.edu/?p=548409

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By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg

What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the latest research and what it means for policymakers. If you’d like to receive the biweekly Essential Scan by email, you can sign up here.

In the latest annual update to the Kaiser Family Foundation’s Employer Health Benefits Survey, researchers observed further increases in premiums for single and family coverage and increased enrollment in high deductible health plans (HDHPs). Specifically, the average annual premium for single coverage rose 3 percent to $6,896 and the average annual premium for family coverage rose 5 percent to $19,616. Although HDHP adoption rates only increased by 1 percent from 2017, for 28 percent of employees at a large firm and 63 percent of employees at small firms, a HDHP was the only insurance option their firm offered. The number of covered workers who face an annual deductible also increased by four percent to 85 percent. The authors of the report warn that the increasing prevalence of self-funded plan options could lead to higher premiums for the fully-insured portion of the small group market and that the repeal of the individual mandate could negatively affect take-up rates in years to come. Full study here.

In recent years local markets for physician services have become increasingly concentrated. A new study uses Medicare claims and enrollment data to examine the effect of cardiology market structure on utilization and health outcomes for four patient populations—those treated for hypertension, a chronic cardiac condition, an acute cardiac condition, or an acute myocardial infarction (AMI). The study found that higher market concentration is associated with higher total expenditures and worse health outcomes. In three of the sample populations, patients residing in a zip code at the 75th percentile of cardiology market concentration were found to have a 5 to 7 percent greater chance of risk-adjusted mortality as compared with identical patients in a zip code at the 25th percentile of market concentration. Researchers also found that there was a 7 to 11 percent increase in expenditures when moving from the 25th percentile to the 75th percentile of market concentration. The negative correlation between concentration and quality of care found in this study indicates that antitrust agencies have reason to be concerned about the effects of consolidation in physician markets on the price and quality of healthcare services. Full study here.

Distinct features of managed care plans may influence continuity of Medicaid enrollment, which is important for provision of Medicaid services, spending, and patient outcomes. A new study examines the impact of managed care plan type on continuous enrollment in Medicaid, leveraging the random assignment of Medicaid enrollees into two managed care plans following the exit of an insurer in one state. The researchers found no significant differences in continuity of coverage between enrollees who were randomly assigned to a mixed, national for-profit plan that serves both Medicaid and commercial populations compared to a Medicaid-focused, local nonprofit plan. The researchers found the main predictors of longer enrollment in Medicaid were greater outpatient utilization and a special healthcare need among children. Given their findings, the researchers conclude, “Random assignment has the potential to balance insurance enrollment across managed care plans and is one equitable strategy for policymakers to implement when auto-assigning enrollees.” Full study here.

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https://www.brookings.edu/wp-content/uploads/2017/03/topicimg_medicareandmedicaid.jpg?w=270By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg
What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the latest research and what it means for policymakers. If you’d like to receive the biweekly Essential Scan by email, you can sign up here.
Premiums and Contributions Continue to Rise for Employer Sponsored Healthcare
Study by:Gary Claxton, Matthew Rae, Michelle Long, Anthony Damico, and Heidi Whitmore
In the latest annual update to the Kaiser Family Foundation’s Employer Health Benefits Survey, researchers observed further increases in premiums for single and family coverage and increased enrollment in high deductible health plans (HDHPs). Specifically, the average annual premium for single coverage rose 3 percent to $6,896 and the average annual premium for family coverage rose 5 percent to $19,616. Although HDHP adoption rates only increased by 1 percent from 2017, for 28 percent of employees at a large firm and 63 percent of employees at small firms, a HDHP was the only insurance option their firm offered. The number of covered workers who face an annual deductible also increased by four percent to 85 percent. The authors of the report warn that the increasing prevalence of self-funded plan options could lead to higher premiums for the fully-insured portion of the small group market and that the repeal of the individual mandate could negatively affect take-up rates in years to come. Full study here.
Average Annual Premiums for Single and Family Coverage, 1999-2018
Distribution of Health Plan Enrollment for Covered Workers, by Plan Type, 1988-2018
________________________________________________________
Concentration in Cardiology Markets Associated with Higher Prices and Lower Quality of Care
Study by: Thomas Koch, Brett Wendling, and Nathan E. Wilson
In recent years local markets for physician services have become increasingly concentrated. A new study uses Medicare claims and enrollment data to examine the effect of cardiology market structure on utilization and health outcomes for four patient populations—those treated for hypertension, a chronic cardiac condition, an acute cardiac condition, or an acute myocardial infarction (AMI). The study found that higher market concentration is associated with higher total expenditures and worse health outcomes. In three of the sample populations, patients residing in a zip code at the 75th percentile of cardiology market concentration were found to have a 5 to 7 percent greater chance of risk-adjusted mortality as compared with identical patients in a zip code at the 25th percentile of market concentration. Researchers also found that there was a 7 to 11 percent increase in expenditures when moving from the 25th percentile to the 75th percentile of market concentration. The negative correlation between concentration and quality of care found in this study indicates that antitrust agencies have reason to be concerned about the effects of consolidation in physician markets on the price and quality of healthcare services. Full study here. ________________________________________________________
Medicaid Managed Care Plan Type Not Associated with Differences in Continuity of Coverage
Study by: Sarah H. Gordon, Yoojin Lee, Chima D. Ndumele, et al.
Distinct features of managed care plans may influence continuity of Medicaid enrollment, which is important for provision of Medicaid services, spending, and patient outcomes. A new study examines the impact of managed care plan type on continuous enrollment in Medicaid, leveraging the random assignment of Medicaid enrollees into two managed care plans following the exit of an insurer in one state. The researchers found no significant differences in continuity of coverage between enrollees who were randomly assigned to a mixed, national ... By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg
What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the ... https://www.brookings.edu/experts/christen-linke-young/Christen Linke Younghttp://webfeeds.brookings.edu/~/580148298/0/brookingsrss/topics/healthit~Christen-Linke-Young/
Wed, 14 Nov 2018 13:07:21 +0000https://www.brookings.edu/?post_type=expert&p=547423

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By Kevin Thibodeaux

Christen Linke Young is a fellow with the USC-Brookings Schaeffer Initiative for Health Policy. Previously, she was the Principal Deputy Secretary for the North Carolina Department of Health and Human Services, where she focused on developing cross-agency initiatives, implementing innovative policy solutions, and providing day-to-day operational leadership of the Department.

She also previously held a number of roles in the federal government. She was Principal Deputy Director of the Center for Consumer Information and Insurance Oversight, the agency within the Center for Medicare & Medicaid Services that oversees private health insurance initiatives. In addition, she served as the Senior Policy Advisor for Health at the White House and the Director of Coverage Policy in the U.S. Department of Health and Human Services’ Office of Health Reform. She began her career in government as a policy analyst with the Centers for Disease Control and Prevention.

Linke Young holds a bachelor’s degree in biological sciences from Stanford University and a law degree from Yale Law School.

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By Kevin Thibodeaux
Christen Linke Young is a fellow with the USC-Brookings Schaeffer Initiative for Health Policy. Previously, she was the Principal Deputy Secretary for the North Carolina Department of Health and Human Services, where she focused on developing cross-agency initiatives, implementing innovative policy solutions, and providing day-to-day operational leadership of the Department.
She also previously held a number of roles in the federal government. She was Principal Deputy Director of the Center for Consumer Information and Insurance Oversight, the agency within the Center for Medicare & Medicaid Services that oversees private health insurance initiatives. In addition, she served as the Senior Policy Advisor for Health at the White House and the Director of Coverage Policy in the U.S. Department of Health and Human Services’ Office of Health Reform. She began her career in government as a policy analyst with the Centers for Disease Control and Prevention.
Linke Young holds a bachelor’s degree in biological sciences from Stanford University and a law degree from Yale Law School. By Kevin Thibodeaux
Christen Linke Young is a fellow with the USC-Brookings Schaeffer Initiative for Health Policy. Previously, she was the Principal Deputy Secretary for the North Carolina Department of Health and Human Services, where she ... https://www.brookings.edu/blog/usc-brookings-schaeffer-on-health-policy/2018/11/05/the-essential-scan-top-findings-in-health-policy-research-50/The essential scan: Top findings in health policy researchhttp://webfeeds.brookings.edu/~/578520430/0/brookingsrss/topics/healthit~The-essential-scan-Top-findings-in-health-policy-research/
Mon, 05 Nov 2018 15:44:45 +0000https://www.brookings.edu/?p=546497

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By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg

What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the latest research and what it means for policymakers. If you’d like to receive the biweekly Essential Scan by email, you can sign up here.

In late 2017, President Trump signed major tax legislation into law that eliminated the penalties associated with the Affordable Care Act’s individual mandate, effectively repealing the requirement. Mandate repeal is expected to lead to substantially higher individual market health insurance premiums and higher rates of uninsurance. A new paper provides a blueprint for states considering instituting their own individual mandates at the state level, using both federal law and Massachusetts law as starting points. According to the author, the key elements of the proposed mandate legislation include: (1) the types of coverage that qualify; (2) the amount of penalties for not maintaining coverage; and (3) the exemptions available. The author notes that instituting a mandate is a straightforward way to protect state health insurance markets by restoring rules in effect under the ACA. It can also offer other potential benefits like limiting the impact of substandard plans, and facilitating coverage outreach through the use of penalty funds. Full report here.

Although highly effective hepatitis C therapies were introduced to the market five years ago, they continue to be out of reach for the majority of patients infected because of the high upfront costs. The problem of high upfront costs is further exacerbated by the fact that many patients with hepatitis C receive care from Medicaid and state prisons-which face increasingly tight budget constraints. A new viewpoint puts forth a “Netflix Model” for HCV therapies whereby a purchasing coalition constituting all payers for healthcare would leverage their scale for purchasing, streamline access to all patients, and ensure payers collectively recapture the long-term savings from avoided future medical costs. The authors outline the steps to implementation and regulatory questions state payers would need to work through. Dana Goldman and Neeraj Sood have put forth similar subscription-based pricing models and applied them to HCV treatment within the Medicaid population to better align the long-term value of many of these drugs with the upfront price. As policymakers and payers grapple with how to manage access to new high-priced, high-value pharmaceuticals, alternative payment strategies like subscription-based purchasing may be the key to expanding access while appropriately valuing innovation. Full viewpoint here.

Under the ACA, each state is divided into a set number of geographic “rating areas,” which were primarily based on differences in healthcare provider prices and often cut across several counties. While insurers that want to participate in the marketplaces are required to price their health insurance plan uniformly within the rating area, insurers are not required to sell in all counties within the rating area. A new paper analyzes insurer behavior within this framework, specifically why insurers enter some but not all of the counties in a rating area (a behavior termed partial rating area offering). They find that about 30 percent of insurance companies participating in the marketplaces exclude at least one county from their service area while selling to other counties in the same rating area. Based on their model, the researchers find that the driving factor for this behavior is avoidance of counties with relatively fewer marketplace enrollees and higher share of unhealthy consumers. Given that many counties now have fewer insurers operating in the ACA marketplaces, policymakers may want to consider providing insurers with subsidies or other incentives tied to specific service areas rather than the rating area as a whole. Full study here.

A new study measures between-payer and between-provider price variation in Massachusetts. Using data from the Massachusetts All-Payer Claims Database, researchers examined variations in prices paid to the same provider across five well-defined services in addition to overall price levels for inpatient care. Across the five clinical cohorts, the standard deviation in prices across hospitals ranged from 17-31 percent of the mean, controlling for the payer. The standard deviation in prices across payers ranged from 16-28 percent of the mean, controlling for hospitals. Variation in prices across payers affects the value of insurance products. Researchers noted that prices paid are higher for administrative-services-only contracts holding fixed both payer and provider and that while insurer size did not necessarily predict negotiated rates, the ability to “steer” consumer demand towards specific providers was important for negotiating better rates. The paper models negotiation incentives and shows that contractual form and demand responsiveness to negotiated prices are important determinants of negotiated prices. Documentation of this variation in provider negotiated prices is important given current trends in health care costs as well as the move towards high deductible health care plans with larger consumer financial burden. Full study here.

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By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg
What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the latest research and what it means for policymakers. If you’d like to receive the biweekly Essential Scan by email, you can sign up here.
Understanding the Hows and Whys of Implementing State-Based Individual Mandates
Report by: Jason Levitis
In late 2017, President Trump signed major tax legislation into law that eliminated the penalties associated with the Affordable Care Act's individual mandate, effectively repealing the requirement. Mandate repeal is expected to lead to substantially higher individual market health insurance premiums and higher rates of uninsurance. A new paper provides a blueprint for states considering instituting their own individual mandates at the state level, using both federal law and Massachusetts law as starting points. According to the author, the key elements of the proposed mandate legislation include: (1) the types of coverage that qualify; (2) the amount of penalties for not maintaining coverage; and (3) the exemptions available. The author notes that instituting a mandate is a straightforward way to protect state health insurance markets by restoring rules in effect under the ACA. It can also offer other potential benefits like limiting the impact of substandard plans, and facilitating coverage outreach through the use of penalty funds. Full report here.________________________________________________________
Alternative State-Level Financing For Hepatitis C Treatment Key to Expanding Access to Infected Populations
Viewpoint by: Mark R. Trusheim, William B. Cassidy, and Peter B. Bach
Although highly effective hepatitis C therapies were introduced to the market five years ago, they continue to be out of reach for the majority of patients infected because of the high upfront costs. The problem of high upfront costs is further exacerbated by the fact that many patients with hepatitis C receive care from Medicaid and state prisons-which face increasingly tight budget constraints. A new viewpoint puts forth a “Netflix Model” for HCV therapies whereby a purchasing coalition constituting all payers for healthcare would leverage their scale for purchasing, streamline access to all patients, and ensure payers collectively recapture the long-term savings from avoided future medical costs. The authors outline the steps to implementation and regulatory questions state payers would need to work through. Dana Goldman and Neeraj Sood have put forth similar subscription-based pricing models and applied them to HCV treatment within the Medicaid population to better align the long-term value of many of these drugs with the upfront price. As policymakers and payers grapple with how to manage access to new high-priced, high-value pharmaceuticals, alternative payment strategies like subscription-based purchasing may be the key to expanding access while appropriately valuing innovation. Full viewpoint here. ________________________________________________________
Partial Rating Area Offering in the ACA Marketplaces Driven by Health Measures of Populations
Study by: Hanming Fang and Ami Ko
Under the ACA, each state is divided into a set number of geographic “rating areas,” which were primarily based on differences in healthcare provider prices and often cut across several counties. While insurers that want to participate in the marketplaces are required to price their health insurance plan uniformly within the rating area, insurers are not required to sell in all counties within the rating area. A new paper analyzes insurer behavior within this framework, specifically why insurers enter some but not all of the counties in a rating area (a behavior termed partial rating area offering). They find that ... By Abigail Durak, Stephanie Hedt, Will Palmisano, Paul Ginsburg
What’s the latest in health policy research? The Essential Scan, produced by the USC-Brookings Schaeffer Initiative for Health Policy, aims to help keep you informed on the ... https://www.brookings.edu/research/the-opportunities-and-challenges-of-data-analytics-in-health-care/The opportunities and challenges of data analytics in health carehttp://webfeeds.brookings.edu/~/577842700/0/brookingsrss/topics/healthit~The-opportunities-and-challenges-of-data-analytics-in-health-care/
Thu, 01 Nov 2018 04:01:03 +0000https://www.brookings.edu/?post_type=research&p=545781

Data analytics tools have the potential to transform health care in many different ways. In the near future, routine doctor’s visits may be replaced by regularly monitoring one’s health status and remote consultations. The inpatient setting will be improved by more sophisticated quality metrics drawn from an ecosystem of interconnected digital health tools. The care patients receive may be decided in consultation with decision support software that is informed not only by expert judgments but also by algorithms that draw on information from patients around the world, some of whom will differ from the “typical” patient. Support may be customized for an individual’s personal genetic information, and doctors and nurses will be skilled interpreters of advanced ways to diagnose, track, and treat illnesses. In a number of different ways, policymakers are likely to have new tools that provide valuable insights into complicated health, treatment, and spending trends.

However, recent developments in data analytics also suggest barriers to change that might be more substantial in the health care field than in other parts of the economy. Despite the immense promise of health analytics, the industry lags behind other major sectors in taking advantage of cutting-edge tools. Most health care organizations, for example, have yet to devise a clear approach for integrating data analytics into their regular operations. One study even showed that 56 percent of hospitals have no strategies for data governance or analytics.

Compared to other industries, the slow pace of innovation reflects challenges that are unique to health care in implementing and applying “big data” tools. These barriers include the nature of health care decisions, problematic data conventions, institutionalized practices in care delivery, and the misaligned incentives of various actors in the industry. To address these barriers, federal policy should emphasize interoperability of health data and prioritize payment reforms that will encourage providers to develop data analytics capabilities.

Despite the immense promise of health analytics, the industry lags behind other major sectors in taking advantage of cutting-edge tools.

Sensitivity of care decisions

A major barrier to the widespread application of data analytics in health care is the nature of the decisions and the data themselves. Unlike many other industries, health care decisions deal with hugely sensitive information, require timely information and action, and sometimes have life or death consequences. Each of these features creates a barrier to the pervasive use of data analytics.

The immediacy of health care decisions requires regular monitoring of data and extensive staffing and infrastructure to collect and tabulate information. The nature of health care decisions are more immediate and intrinsic than those made in other settings, creating a hesitancy about overhauling any major aspect of care provision. Health care decisions must take into account patient preferences, which at times differ from expert recommendations.

The importance and complexity of these decisions means physicians and patients insist on very high standards for data-analytics tools in health care. That has proven very challenging to designers of these tools, as health providers are more accustomed to dealing with either broad knowledge or narrow choices rather than complex predictions that require careful identification of decisions and calibration of predictions. As a result, clinical decision support software has struggled to make better insights than physicians. Even one of the most advanced systems, IBM’s Watson, made a series of “unsafe and incorrect treatment recommendations” because it was calibrated based on synthetic cases rather than real patient data. There is risk even when training software uses real patient data because decision support software may overfit its models and thereby make less useful suggestions, such as prescribing an inappropriate treatment plan. Sometimes, the clinically best medical decision is not always what a patient wants to pursue.

The sensitive nature of health care decisions and data furthermore creates major concerns about privacy. Patients are rightfully concerned about the security of their data and concerned about it being used in ways that are detrimental to them, damage their reputations, or disadvantage them in the rating and marketing decisions of insurers. This isn’t limited to medical record data. Recent news coverage of the capture of the Golden State Killer, for example, has raised new questions about the privacy of direct-to-consumer genetic testing. And while the growth of “wearables” such as FitBit and Nike+ FuelBand have made health status monitoring accessible to patients, these data are not subjected to federal patient privacy laws, allowing these companies to design their own internal privacy policies and share information with third-parties.

Problematic data conventions

Several data conventions in health care hinder the widespread use of data analytics. Currently, health care data are split among different entities and have different formats such that building an insightful, granular database is next to impossible. These qualities greatly increase the cost of using data to provide value, even when all the relevant information has been recorded in some form. Furthermore, even well-structured data are often not available to researchers or providers who could use them in useful ways.

Several data conventions in health care hinder the widespread use of data analytics. Currently, health care data are split among different entities and have different formats such that building an insightful, granular database is next to impossible.

In general, the health care industry has been resistant to making information available as open data commons, which are up-to-date data provided in accessible format and available to all. That resistance comes in part from fear of violating privacy, even though existing strategies for protecting confidentiality greatly mitigate that risk. A larger reason is that data commons are a public good and will naturally be undersupplied by the market. A third data challenge is data quality. For analysis or predictions to have any value, they must be based on good data. One of the most hyped applications of big data in epidemiology, Google Flu Trends, turned out to underperform far more basic models, despite analyzing far more data, because its analysts were extrapolating from the behavior of Google users—an unrepresentative group of people. The experience illustrated that the success of data analytics in health care is dependent upon the availability and utilization of quality data.

Institutional practices

Entrenched practices in the delivery of health care also create several barriers to the full adoption of data analytics. One clear illustration of the challenge is in one of the most promising areas of data analytics: clinical decision support. While data analytics could greatly improve the clinical decision-making process, the development of decision support tools hasn’t paid sufficient attention to how decisions are actually made and the related workflows supporting those decisions. The tools often assume that putting the right information on a single person’s dashboard can induce them to make the right decision, but in reality, most difficult clinical decisions involve many actors and often follow institutional guidelines designed by committees. Data tools that do not fit into existing work and decision-making structures add burdens to physicians and are much less effective than they could be. For example, many attempts to bring data analytics or other information technology into health care have created a large data entry burden for physicians. This had led to high-profile mistakes, physician burnout, and general dissatisfaction with the tools.

As a consequence, most of the major reasons physicians cite for their resistance to adoption of new data tools are related to workflow disruption. For data analytics to truly transform care, the designers of tools need to cognizant of the context their tools will be used in and health care organizations must be willing to reorganize some elements of their practice to empower patients and providers to use data-driven care.

Misaligned incentives

Arguably the largest barrier to the implementation and application of data analytics in health care is the splintered landscape of the industry, with separate components having their own incentives that diverge from what might be best for the entire system. At the moment, physicians or delivery systems may not know that their patients have visited emergency rooms, for example, unless told by the insurer—because claims data are held by the payer. Meanwhile, care providers may hold clinical data that could help insurers better manage their patient’s costs. The responsibility for managing any given patient is split between their insurer and various providers, each with different incentives and needs and neither functioning as an ideal agent for the patient.

Insurers have incentives to invest in better health for their covered population, but these incentives are mitigated by annual contracts with employers or individuals and employee turnover, which moves many enrollees to a different insurer before the payer’s investments in their health pay off. There are also serious concerns with expecting insurers to take the lead on data analytics in health care. First, data tools designed for insurers are likely to center on costs, which may leave some quality-enhancing insights unexplored. Second, insurer data analytics may impose an externality on hospitals and physicians, which have to bear the administrative costs of complying with the data practices of various insurers. Third, insurers may not conduct their data analytics on a clinically useful timetable. Unless they feed data to providers continuously, it may not be timely enough to affect how patients receive care. The limited degree to which insurers provide claims data to providers that they contract with may reflect the expense of doing so, limitations in their legacy IT systems, or a desire to retain more of the care management responsibility.

The responsibility for managing any given patient is split between their insurer and various providers, each with different incentives and needs and neither functioning as an ideal agent for the patient.

Health care providers have their own particular incentives. Under the most common payment schemes, providers typically have little incentive to control patient costs. However, they likely do care about quality of care, even if they are hesitant to change their institutional practices and norms. Despite seeming like a more logical locus for data decisions, hospitals are often unwilling to undertake the costs of developing data capabilities or the disruption of implementing their use into regular practice. Hospitals also have an incentive to slow health information exchange standards because the lack of interoperability binds physicians into referral patterns favorable to them. Similarly, vendors of health information technology often don’t want standardization of data tools and practices because differentiation of their products and high costs for providers that switch vendors create substantial monopoly power for vendors. Finally, patients themselves often don’t support data practices that can improve care for all. The fear of data breaches or misuse leads patients to oppose data sharing arrangements that may have widespread positive externalities. In short, no individual actor in the health care space has the incentives or means to fully embrace the most revolutionary data analytics practices.

Policy recommendations

Because of the systemic challenges described above, we need policy changes that diminish the barriers to health analytics. While there is potential for radical overhaul, the initial priority should be making sure all hospitals can record, use, and share patient data in useful ways. One critical component of that agenda is ensuring interoperability of Electronic Medical Records (EMRs). Federal policy has contributed a great deal to the adoption of EMRs and other health IT practices through incentives under the Medicare program, but providers still struggle with sharing that data. As discussed above, neither hospitals nor EMR vendors have a strong incentive to standardize health information exchanges, despite the fact that interoperable EMRs can improve care and save money. The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act included health information exchange as one of the required capabilities for certified EMR systems. However, this requirement was included at a later implementation stage, allowing EMR systems to be designed and integrated into health systems without these capabilities, making interoperability even more difficult. In 2016, the 21st Century Cures Act increased incentives and penalties specifically promoting EMR interoperability.

These incentives need not aim to establish one universal EMR. Applications that can access and transfer health data from different kinds of EMRs can achieve interoperability, but they are not used as widely or thoroughly as possible, risking a situation where the applications meant to bridge different EMRs themselves fail to adopt uniform data conventions. Federal policy could standardize the way EMR data are accessed and transferred by applications, like Fast Healthcare Interoperability Resources (FHIR), that exist to facilitate interoperability. It could also revise HITECH and the Health Insurance Portability and Accountability Act (HIPAA) to allow fees for data exchange, thus creating incentives to improve data exchange that could potentially counteract the existing disincentives. Federal support for best practices in data management and use would go a long way in helping the industry develop its own capabilities.

The federal government can also indirectly support the development of health data analytics by continuing to encourage payment based on the value of care, typically through the Medicare program, encouraging alternative payment approaches, and by working to align quality measures and payment approaches with private insurers. Under value-based care models, providers are typically paid some amount per beneficiary based on the package of care they are expected to deliver, with payment at least partially tied to quality-of-care metrics. These models aim to create the incentive for providers to provide high-quality care at lower costs, which often involves closer coordination of care and careful revision of many practices. All these features make hospitals operating under value-based care models better loci for data-backed decisions. Kaiser Permanente has demonstrated the power of a well-integrated data strategy aimed at managing costs and quality. Conversely, improved data analytics capabilities may be precisely what health care providers need to better coordinate and improve value of care. Medicare could improve the usability of its data for a wider audience with a varying degree of analytic capabilities to help more of these providers successfully implement these new health care models. Coupling these systemic health care reforms can allow them to complement each other and reduce administrative confusion.

Federal support for best practices in data management and use would go a long way in helping the industry develop its own capabilities.

One factor that is holding back progress toward value-based payment is risk adjustment—varying the payment on the basis of how challenging one provider’s patients are in comparison to other providers. Much of the energy in improving risk adjustment has focused on contracts between purchasers and insurers—for example, between the Medicare program and Medicare Advantage plans. But the risk adjustment challenges for contracts between insurers and providers are distinct from these and, if ignored, pose grave challenges to some of the best providers, who inevitably attract patients with the most challenging conditions.

Despite the disruptions to conventional practices, all actors in health care should be excited about the possibilities that new data tools will bring. But obtaining this enormous potential is not around the corner and will require overcoming challenges by all of the relevant components of the health care system.

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By Paul Ginsburg, Andrés de Loera-Brust, Caitlin Brandt, Abigail Durak
Data analytics tools have the potential to transform health care in many different ways. In the near future, routine doctor's visits may be replaced by regularly monitoring one’s health status and remote consultations. The inpatient setting will be improved by more sophisticated quality metrics drawn from an ecosystem of interconnected digital health tools. The care patients receive may be decided in consultation with decision support software that is informed not only by expert judgments but also by algorithms that draw on information from patients around the world, some of whom will differ from the “typical” patient. Support may be customized for an individual’s personal genetic information, and doctors and nurses will be skilled interpreters of advanced ways to diagnose, track, and treat illnesses. In a number of different ways, policymakers are likely to have new tools that provide valuable insights into complicated health, treatment, and spending trends.
However, recent developments in data analytics also suggest barriers to change that might be more substantial in the health care field than in other parts of the economy. Despite the immense promise of health analytics, the industry lags behind other major sectors in taking advantage of cutting-edge tools. Most health care organizations, for example, have yet to devise a clear approach for integrating data analytics into their regular operations. One study even showed that 56 percent of hospitals have no strategies for data governance or analytics.
Compared to other industries, the slow pace of innovation reflects challenges that are unique to health care in implementing and applying “big data” tools. These barriers include the nature of health care decisions, problematic data conventions, institutionalized practices in care delivery, and the misaligned incentives of various actors in the industry. To address these barriers, federal policy should emphasize interoperability of health data and prioritize payment reforms that will encourage providers to develop data analytics capabilities.
Despite the immense promise of health analytics, the industry lags behind other major sectors in taking advantage of cutting-edge tools.
Sensitivity of care decisions
A major barrier to the widespread application of data analytics in health care is the nature of the decisions and the data themselves. Unlike many other industries, health care decisions deal with hugely sensitive information, require timely information and action, and sometimes have life or death consequences. Each of these features creates a barrier to the pervasive use of data analytics.
The immediacy of health care decisions requires regular monitoring of data and extensive staffing and infrastructure to collect and tabulate information. The nature of health care decisions are more immediate and intrinsic than those made in other settings, creating a hesitancy about overhauling any major aspect of care provision. Health care decisions must take into account patient preferences, which at times differ from expert recommendations.
The importance and complexity of these decisions means physicians and patients insist on very high standards for data-analytics tools in health care. That has proven very challenging to designers of these tools, as health providers are more accustomed to dealing with either broad knowledge or narrow choices rather than complex predictions that require careful identification of decisions and calibration of predictions. As a result, clinical decision support software has struggled to make better insights than physicians. Even one of the most advanced systems, IBM’s Watson, made a series of “unsafe and incorrect treatment recommendations” because it was calibrated based on synthetic cases rather than real patient data. There is risk even when training ... By Paul Ginsburg, Andrés de Loera-Brust, Caitlin Brandt, Abigail Durak
Data analytics tools have the potential to transform health care in many different ways. In the near future, routine doctor's visits may be replaced by regularly monitoring ... https://www.brookings.edu/events/23rd-annual-wall-street-comes-to-washington-health-care-roundtable/23rd annual Wall Street Comes to Washington Health Care Roundtablehttp://webfeeds.brookings.edu/~/577730562/0/brookingsrss/topics/healthit~rd-annual-Wall-Street-Comes-to-Washington-Health-Care-Roundtable/
Wed, 31 Oct 2018 20:05:08 +0000https://www.brookings.edu/?post_type=event&p=545780